TRABAJO 04¶

Alumno: Carlos Mansilla

Analytics on GeodataFrames¶

Revisamos la data:

In [781]:
import pandas as pd
linkData="C:\\Users\\LENOVO\\Desktop\\Semana 10\\covid_contagiados.csv"
covid = pd.read_csv(linkData)
covid.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 4585360 entries, 0 to 4585359
Data columns (total 10 columns):
 #   Column           Dtype  
---  ------           -----  
 0   FECHA_CORTE      int64  
 1   DEPARTAMENTO     object 
 2   PROVINCIA        object 
 3   DISTRITO         object 
 4   METODODX         object 
 5   EDAD             float64
 6   SEXO             object 
 7   FECHA_RESULTADO  float64
 8   UBIGEO           float64
 9   id_persona       float64
dtypes: float64(4), int64(1), object(5)
memory usage: 349.8+ MB
In [783]:
covid.head()
Out[783]:
FECHA_CORTE DEPARTAMENTO PROVINCIA DISTRITO METODODX EDAD SEXO FECHA_RESULTADO UBIGEO id_persona
0 20241203 TUMBES TUMBES TUMBES AG 46.0 FEMENINO 20221207.0 240101.0 203499.0
1 20241203 LIMA LIMA JESUS MARIA AG 69.0 FEMENINO 20230822.0 150113.0 221397.0
2 20241203 SAN MARTIN MOYOBAMBA MOYOBAMBA AG 55.0 FEMENINO 20240108.0 220101.0 295651.0
3 20241203 AREQUIPA CAYLLOMA COPORAQUE AG 50.0 MASCULINO 20230824.0 40506.0 851625.0
4 20241203 LIMA LIMA JESUS MARIA AG 58.0 MASCULINO 20221217.0 150113.0 287786.0
In [785]:
covid['FECHA_RESULTADO'] = covid['FECHA_RESULTADO'].astype(str).str[:-4]

# Mostrar las primeras filas para verificar el cambio
print(covid[['FECHA_RESULTADO']].head())

  FECHA_RESULTADO
0          202212
1          202308
2          202401
3          202308
4          202212
In [786]:
# years in data
covid.FECHA_RESULTADO.value_counts()
Out[786]:
FECHA_RESULTADO
202201    1010098
202207     286577
202008     243265
202104     242400
202103     235429
202202     216536
202102     195843
202212     184993
202208     184170
202009     157189
202105     148467
202007     144012
202101     133867
202005     131513
202006     121560
202211     113985
202106      95254
202010      81303
202112      60846
202107      59960
202206      53342
202011      48244
202012      47717
202004      46500
202111      41644
202108      35360
202109      30043
202110      28762
202209      26748
202312      26289
202401      24721
202203      24168
202301      17829
202205      12933
202204      10337
202304       9121
202210       8126
202303       7589
202305       6163
202311       5569
202306       5403
202302       4213
202307       3214
202309       3147
202310       2829
202402       2274
             2023
202308       1995
202003       1317
189912        394
202403         79
Name: count, dtype: int64

Eliminamos datos NAs de "FECHA_RESULTADO":

In [790]:
covid['FECHA_RESULTADO'] = pd.to_numeric(covid['FECHA_RESULTADO'], errors='coerce')
covid = covid.dropna(subset=['FECHA_RESULTADO'])

Recodificamos la variable a numérica para poder filtrarla:

In [792]:
covid['FECHA_RESULTADO'] = covid['FECHA_RESULTADO'].astype(int)

Leemos el subset:

In [796]:
covid = covid[(covid.FECHA_RESULTADO >= 202000) & 
              (covid.FECHA_RESULTADO <= 202199) & 
              (covid.PROVINCIA != "EN INVESTIGACIÓN")]
covid.FECHA_RESULTADO.value_counts()
Out[796]:
FECHA_RESULTADO
202008    230258
202104    229819
202103    221141
202102    183990
202009    147985
202105    141260
202007    135799
202005    129951
202101    126279
202006    116148
202106     90488
202010     76135
202112     59152
202107     57014
202004     45911
202011     45491
202012     44705
202111     39128
202108     34134
202109     28235
202110     26903
202003      1302
Name: count, dtype: int64

Recategorizamos

In [799]:
labels = ["Niños", "Adolescentes", "Jovenes_Adultos", "Adultos_Edad_Media", "Adultos_Mayores"]
covid['CATEGORIA_EDAD'] = pd.cut(covid['EDAD'], bins=[0, 9, 19, 39, 59, float('inf')], labels=labels)

Tenemos la distribución de contagiados por edad:

In [802]:
covid.CATEGORIA_EDAD.value_counts()
Out[802]:
CATEGORIA_EDAD
Jovenes_Adultos       907276
Adultos_Edad_Media    747203
Adultos_Mayores       376492
Adolescentes          128428
Niños                  43591
Name: count, dtype: int64

Vemos las posibilidades:

In [807]:
#Por si queremos ver el fenómeno quitando a los Niños
covid_contagio=covid[covid.CATEGORIA_EDAD!='Niños']

covid_contagio.head()
Out[807]:
FECHA_CORTE DEPARTAMENTO PROVINCIA DISTRITO METODODX EDAD SEXO FECHA_RESULTADO UBIGEO id_persona CATEGORIA_EDAD
9 20241203 TUMBES ZARUMILLA AGUAS VERDES AG 52.0 MASCULINO 202012 240302.0 233066.0 Adultos_Edad_Media
10 20241203 TUMBES ZARUMILLA ZARUMILLA AG 42.0 FEMENINO 202012 240301.0 307679.0 Adultos_Edad_Media
27 20241203 TACNA TACNA ALTO DE LA ALIANZA AG 58.0 FEMENINO 202101 230102.0 256563.0 Adultos_Edad_Media
31 20241203 SAN MARTIN SAN MARTIN TARAPOTO AG 48.0 MASCULINO 202012 220901.0 301845.0 Adultos_Edad_Media
38 20241203 TACNA TACNA TACNA AG 45.0 FEMENINO 202101 230101.0 348484.0 Adultos_Edad_Media

Reshaping to Long¶

CONTAGIADOS¶

Personas contagiadas por provincia, por departamento, por año y mes:

In [811]:
indexList=['FECHA_RESULTADO','DEPARTAMENTO','PROVINCIA', 'CATEGORIA_EDAD']
aggregator={'CATEGORIA_EDAD':[len]}
covid_provYear=covid_contagio.groupby(indexList,observed=True).agg(aggregator)
covid_provYear
Out[811]:
CATEGORIA_EDAD
len
FECHA_RESULTADO DEPARTAMENTO PROVINCIA CATEGORIA_EDAD
202003 ANCASH HUARAZ Adolescentes 1
SANTA Jovenes_Adultos 2
Adultos_Edad_Media 9
Adultos_Mayores 1
AREQUIPA AREQUIPA Adolescentes 2
... ... ... ... ...
202112 UCAYALI CORONEL PORTILLO Adultos_Mayores 25
PADRE ABAD Adolescentes 13
Jovenes_Adultos 35
Adultos_Edad_Media 17
Adultos_Mayores 5

14952 rows × 1 columns

Mandamos los counts a las "wide columns":

In [814]:
covidDraft=covid_provYear.unstack(3).fillna(0) #leftmost index in rows
covidDraft
Out[814]:
CATEGORIA_EDAD
len
CATEGORIA_EDAD Adolescentes Jovenes_Adultos Adultos_Edad_Media Adultos_Mayores
FECHA_RESULTADO DEPARTAMENTO PROVINCIA
202003 ANCASH HUARAZ 1.0 0.0 0.0 0.0
SANTA 0.0 2.0 9.0 1.0
AREQUIPA AREQUIPA 2.0 8.0 8.0 11.0
CARAVELI 0.0 1.0 0.0 0.0
AYACUCHO HUAMANGA 0.0 0.0 1.0 0.0
... ... ... ... ... ... ...
202112 TUMBES CONTRALMIRANTE VILLAR 1.0 14.0 7.0 5.0
TUMBES 11.0 77.0 75.0 33.0
ZARUMILLA 6.0 24.0 23.0 12.0
UCAYALI CORONEL PORTILLO 1.0 30.0 16.0 25.0
PADRE ABAD 13.0 35.0 17.0 5.0

4061 rows × 4 columns

Cuota informática de covid, Adultos Mayores:

In [817]:
covidDraft['Adultos_Mayores_pct'] = covidDraft.iloc[:, 1] / (covidDraft.iloc[:, 0] + covidDraft.iloc[:, 1] + covidDraft.iloc[:, 2] + covidDraft.iloc[:, 3])
covid_provYear_Adultos_Mayores_w=covidDraft['Adultos_Mayores_pct'].unstack('FECHA_RESULTADO').fillna(0)
covid_provYear_Adultos_Mayores_w
Out[817]:
FECHA_RESULTADO 202003 202004 202005 202006 202007 202008 202009 202010 202011 202012 ... 202103 202104 202105 202106 202107 202108 202109 202110 202111 202112
DEPARTAMENTO PROVINCIA
AMAZONAS BAGUA 0.00 0.250000 0.559524 0.355491 0.396988 0.364357 0.338021 0.369072 0.407407 0.425926 ... 0.480349 0.386986 0.382625 0.401180 0.381818 0.373626 0.443182 0.522613 0.453287 0.400631
BONGARA 0.00 0.333333 0.555556 0.238095 0.425926 0.349398 0.388889 0.275000 0.272727 0.590909 ... 0.436242 0.423237 0.442424 0.389610 0.548387 0.366667 0.533333 0.320000 0.187500 0.341463
CHACHAPOYAS 0.00 0.482759 0.477528 0.557377 0.508197 0.476596 0.403448 0.547988 0.508772 0.553846 ... 0.449951 0.474308 0.468127 0.448387 0.463415 0.566667 0.592593 0.615385 0.555556 0.397351
CONDORCANQUI 0.00 0.000000 0.333333 0.524064 0.470363 0.531183 0.520833 0.485577 0.504673 0.494505 ... 0.430769 0.472603 0.513761 0.488095 0.714286 0.500000 0.333333 0.368421 0.333333 0.454545
LUYA 0.00 0.000000 0.700000 0.569892 0.565217 0.489051 0.461538 0.448276 0.562500 0.307692 ... 0.552381 0.435484 0.411168 0.393162 0.431373 0.422222 0.500000 0.235294 0.210526 0.433333
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
TUMBES ZARUMILLA 0.25 0.368421 0.295652 0.345382 0.494565 0.381974 0.366762 0.362500 0.422680 0.525000 ... 0.413793 0.394813 0.389610 0.336735 0.442623 0.381818 0.440476 0.328358 0.255319 0.369231
UCAYALI ATALAYA 0.00 0.000000 0.000000 0.571429 0.397959 0.436090 0.465753 0.482456 0.636364 0.333333 ... 0.372549 0.370370 0.405405 0.375000 0.166667 0.500000 0.333333 0.000000 0.333333 0.000000
CORONEL PORTILLO 0.00 0.438525 0.412000 0.406504 0.354535 0.341037 0.360943 0.327811 0.268139 0.296667 ... 0.407807 0.391324 0.371469 0.408946 0.413223 0.457627 0.488372 0.472222 0.346939 0.416667
PADRE ABAD 0.00 0.250000 0.356589 0.410256 0.301435 0.358804 0.302632 0.430464 0.333333 0.432432 ... 0.432927 0.487500 0.382609 0.565217 0.476190 0.230769 0.250000 0.545455 0.533333 0.500000
PURUS 0.00 0.000000 0.000000 0.000000 0.210526 0.315789 0.360656 0.367742 0.296296 0.000000 ... 0.348837 0.421053 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000

196 rows × 22 columns

Notar el tipo de data

In [820]:
covid_provYear_Adultos_Mayores_w.columns
Out[820]:
Index([202003, 202004, 202005, 202006, 202007, 202008, 202009, 202010, 202011,
       202012, 202101, 202102, 202103, 202104, 202105, 202106, 202107, 202108,
       202109, 202110, 202111, 202112],
      dtype='int32', name='FECHA_RESULTADO')

Deberíamos tener texto y no números:

In [823]:
covid_provYear_Adultos_Mayores_w.columns=['date'+str(x) for x in covid_provYear_Adultos_Mayores_w.columns]
In [825]:
# then
covid_provYear_Adultos_Mayores_w
Out[825]:
date202003 date202004 date202005 date202006 date202007 date202008 date202009 date202010 date202011 date202012 ... date202103 date202104 date202105 date202106 date202107 date202108 date202109 date202110 date202111 date202112
DEPARTAMENTO PROVINCIA
AMAZONAS BAGUA 0.00 0.250000 0.559524 0.355491 0.396988 0.364357 0.338021 0.369072 0.407407 0.425926 ... 0.480349 0.386986 0.382625 0.401180 0.381818 0.373626 0.443182 0.522613 0.453287 0.400631
BONGARA 0.00 0.333333 0.555556 0.238095 0.425926 0.349398 0.388889 0.275000 0.272727 0.590909 ... 0.436242 0.423237 0.442424 0.389610 0.548387 0.366667 0.533333 0.320000 0.187500 0.341463
CHACHAPOYAS 0.00 0.482759 0.477528 0.557377 0.508197 0.476596 0.403448 0.547988 0.508772 0.553846 ... 0.449951 0.474308 0.468127 0.448387 0.463415 0.566667 0.592593 0.615385 0.555556 0.397351
CONDORCANQUI 0.00 0.000000 0.333333 0.524064 0.470363 0.531183 0.520833 0.485577 0.504673 0.494505 ... 0.430769 0.472603 0.513761 0.488095 0.714286 0.500000 0.333333 0.368421 0.333333 0.454545
LUYA 0.00 0.000000 0.700000 0.569892 0.565217 0.489051 0.461538 0.448276 0.562500 0.307692 ... 0.552381 0.435484 0.411168 0.393162 0.431373 0.422222 0.500000 0.235294 0.210526 0.433333
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
TUMBES ZARUMILLA 0.25 0.368421 0.295652 0.345382 0.494565 0.381974 0.366762 0.362500 0.422680 0.525000 ... 0.413793 0.394813 0.389610 0.336735 0.442623 0.381818 0.440476 0.328358 0.255319 0.369231
UCAYALI ATALAYA 0.00 0.000000 0.000000 0.571429 0.397959 0.436090 0.465753 0.482456 0.636364 0.333333 ... 0.372549 0.370370 0.405405 0.375000 0.166667 0.500000 0.333333 0.000000 0.333333 0.000000
CORONEL PORTILLO 0.00 0.438525 0.412000 0.406504 0.354535 0.341037 0.360943 0.327811 0.268139 0.296667 ... 0.407807 0.391324 0.371469 0.408946 0.413223 0.457627 0.488372 0.472222 0.346939 0.416667
PADRE ABAD 0.00 0.250000 0.356589 0.410256 0.301435 0.358804 0.302632 0.430464 0.333333 0.432432 ... 0.432927 0.487500 0.382609 0.565217 0.476190 0.230769 0.250000 0.545455 0.533333 0.500000
PURUS 0.00 0.000000 0.000000 0.000000 0.210526 0.315789 0.360656 0.367742 0.296296 0.000000 ... 0.348837 0.421053 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000

196 rows × 22 columns

In [827]:
# as usual
covid_provYear_Adultos_Mayores_w.reset_index(inplace=True)
covid_provYear_Adultos_Mayores_w
Out[827]:
DEPARTAMENTO PROVINCIA date202003 date202004 date202005 date202006 date202007 date202008 date202009 date202010 ... date202103 date202104 date202105 date202106 date202107 date202108 date202109 date202110 date202111 date202112
0 AMAZONAS BAGUA 0.00 0.250000 0.559524 0.355491 0.396988 0.364357 0.338021 0.369072 ... 0.480349 0.386986 0.382625 0.401180 0.381818 0.373626 0.443182 0.522613 0.453287 0.400631
1 AMAZONAS BONGARA 0.00 0.333333 0.555556 0.238095 0.425926 0.349398 0.388889 0.275000 ... 0.436242 0.423237 0.442424 0.389610 0.548387 0.366667 0.533333 0.320000 0.187500 0.341463
2 AMAZONAS CHACHAPOYAS 0.00 0.482759 0.477528 0.557377 0.508197 0.476596 0.403448 0.547988 ... 0.449951 0.474308 0.468127 0.448387 0.463415 0.566667 0.592593 0.615385 0.555556 0.397351
3 AMAZONAS CONDORCANQUI 0.00 0.000000 0.333333 0.524064 0.470363 0.531183 0.520833 0.485577 ... 0.430769 0.472603 0.513761 0.488095 0.714286 0.500000 0.333333 0.368421 0.333333 0.454545
4 AMAZONAS LUYA 0.00 0.000000 0.700000 0.569892 0.565217 0.489051 0.461538 0.448276 ... 0.552381 0.435484 0.411168 0.393162 0.431373 0.422222 0.500000 0.235294 0.210526 0.433333
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
191 TUMBES ZARUMILLA 0.25 0.368421 0.295652 0.345382 0.494565 0.381974 0.366762 0.362500 ... 0.413793 0.394813 0.389610 0.336735 0.442623 0.381818 0.440476 0.328358 0.255319 0.369231
192 UCAYALI ATALAYA 0.00 0.000000 0.000000 0.571429 0.397959 0.436090 0.465753 0.482456 ... 0.372549 0.370370 0.405405 0.375000 0.166667 0.500000 0.333333 0.000000 0.333333 0.000000
193 UCAYALI CORONEL PORTILLO 0.00 0.438525 0.412000 0.406504 0.354535 0.341037 0.360943 0.327811 ... 0.407807 0.391324 0.371469 0.408946 0.413223 0.457627 0.488372 0.472222 0.346939 0.416667
194 UCAYALI PADRE ABAD 0.00 0.250000 0.356589 0.410256 0.301435 0.358804 0.302632 0.430464 ... 0.432927 0.487500 0.382609 0.565217 0.476190 0.230769 0.250000 0.545455 0.533333 0.500000
195 UCAYALI PURUS 0.00 0.000000 0.000000 0.000000 0.210526 0.315789 0.360656 0.367742 ... 0.348837 0.421053 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000

196 rows × 24 columns

Llamamos al mapa:

In [830]:
mapLink='https://github.com/SocialAnalytics-StrategicIntelligence/GeoDF_Analytics/raw/main/maps/ProvsINEI2023.zip'

import geopandas as gpd

provmap=gpd.read_file(mapLink)

provmap.info()

<class 'geopandas.geodataframe.GeoDataFrame'>
RangeIndex: 196 entries, 0 to 195
Data columns (total 6 columns):
 #   Column      Non-Null Count  Dtype   
---  ------      --------------  -----   
 0   OBJECTID    196 non-null    float64 
 1   CCDD        196 non-null    object  
 2   CCPP        196 non-null    object  
 3   DEPARTAMEN  196 non-null    object  
 4   PROVINCIA   196 non-null    object  
 5   geometry    196 non-null    geometry
dtypes: float64(1), geometry(1), object(4)
memory usage: 9.3+ KB

Creamos una columna para concatenar dos:

In [833]:
provmap['location']=['+'.join(x[0]) for x in zip(provmap.iloc[:,3:5].values)]
provmap.head(10)
Out[833]:
OBJECTID CCDD CCPP DEPARTAMEN PROVINCIA geometry location
0 1.0 01 01 AMAZONAS CHACHAPOYAS POLYGON ((-77.72614 -5.94354, -77.72486 -5.943... AMAZONAS+CHACHAPOYAS
1 2.0 01 02 AMAZONAS BAGUA POLYGON ((-78.61909 -4.51001, -78.61802 -4.510... AMAZONAS+BAGUA
2 3.0 01 03 AMAZONAS BONGARA POLYGON ((-77.72759 -5.1403, -77.72361 -5.1406... AMAZONAS+BONGARA
3 4.0 01 04 AMAZONAS CONDORCANQUI POLYGON ((-77.81399 -2.99278, -77.81483 -2.995... AMAZONAS+CONDORCANQUI
4 5.0 01 05 AMAZONAS LUYA POLYGON ((-78.13023 -5.9037, -78.13011 -5.9041... AMAZONAS+LUYA
5 6.0 01 06 AMAZONAS RODRIGUEZ DE MENDOZA POLYGON ((-77.44452 -6.05002, -77.44387 -6.050... AMAZONAS+RODRIGUEZ DE MENDOZA
6 7.0 01 07 AMAZONAS UTCUBAMBA POLYGON ((-78.09288 -5.36258, -78.09288 -5.364... AMAZONAS+UTCUBAMBA
7 8.0 02 01 ANCASH HUARAZ POLYGON ((-77.3987 -9.35563, -77.39852 -9.3560... ANCASH+HUARAZ
8 9.0 02 02 ANCASH AIJA POLYGON ((-77.61368 -9.649, -77.61241 -9.64975... ANCASH+AIJA
9 10.0 02 03 ANCASH ANTONIO RAYMONDI POLYGON ((-77.08856 -8.97496, -77.08804 -8.975... ANCASH+ANTONIO RAYMONDI

Hacemos lo mismo con el data frame:

In [836]:
covid_provYear_Adultos_Mayores_w['location']=['+'.join(x[0]) for x in zip(covid_provYear_Adultos_Mayores_w.iloc[:,:2].values)]
covid_provYear_Adultos_Mayores_w.head()
Out[836]:
DEPARTAMENTO PROVINCIA date202003 date202004 date202005 date202006 date202007 date202008 date202009 date202010 ... date202104 date202105 date202106 date202107 date202108 date202109 date202110 date202111 date202112 location
0 AMAZONAS BAGUA 0.0 0.250000 0.559524 0.355491 0.396988 0.364357 0.338021 0.369072 ... 0.386986 0.382625 0.401180 0.381818 0.373626 0.443182 0.522613 0.453287 0.400631 AMAZONAS+BAGUA
1 AMAZONAS BONGARA 0.0 0.333333 0.555556 0.238095 0.425926 0.349398 0.388889 0.275000 ... 0.423237 0.442424 0.389610 0.548387 0.366667 0.533333 0.320000 0.187500 0.341463 AMAZONAS+BONGARA
2 AMAZONAS CHACHAPOYAS 0.0 0.482759 0.477528 0.557377 0.508197 0.476596 0.403448 0.547988 ... 0.474308 0.468127 0.448387 0.463415 0.566667 0.592593 0.615385 0.555556 0.397351 AMAZONAS+CHACHAPOYAS
3 AMAZONAS CONDORCANQUI 0.0 0.000000 0.333333 0.524064 0.470363 0.531183 0.520833 0.485577 ... 0.472603 0.513761 0.488095 0.714286 0.500000 0.333333 0.368421 0.333333 0.454545 AMAZONAS+CONDORCANQUI
4 AMAZONAS LUYA 0.0 0.000000 0.700000 0.569892 0.565217 0.489051 0.461538 0.448276 ... 0.435484 0.411168 0.393162 0.431373 0.422222 0.500000 0.235294 0.210526 0.433333 AMAZONAS+LUYA

5 rows × 25 columns

Preprocessing¶

Los nombres de países que no están escritos en inglés pueden venir con algunos símbolos que pueden causar problemas (', ~). Deshagámonos de ellos:

In [839]:
import unidecode


byePunctuation=lambda x: unidecode.unidecode(x)
covid_provYear_Adultos_Mayores_w['location']=covid_provYear_Adultos_Mayores_w['location'].apply(byePunctuation)
provmap['location']=provmap['location'].apply(byePunctuation)

Estaría bien asegurarse de que no aparezca ningún "ghost" entre las palabras:

In [842]:
# replacing dashes and multiple spaces by a simple space
covid_provYear_Adultos_Mayores_w['location'] = covid_provYear_Adultos_Mayores_w.location.str.replace(r"\-|\_|\s+", "", regex=True)
provmap['location'] = provmap.location.str.replace(r"\-|\_|\s+", "", regex=True)

Merging¶

Necesitamos fusionar ambas tablas ahora. Eso puede suceder efectivamente si ambas tablas tienen una columna clave: una columna (o colección de ellas) cuyos valores en una tabla son los mismos en la otra.

No es necesario que la coincidencia sea exacta, pero solo se fusionan los valores comunes en la clave/llave.

Averigüemos qué NO coincide en cada tabla:

In [845]:
nomatch_df=set(covid_provYear_Adultos_Mayores_w.location)- set(provmap.location)
nomatch_gdf=set(provmap.location)-set(covid_provYear_Adultos_Mayores_w.location)

Esto es lo que no coincide:

In [848]:
len(nomatch_df), len(nomatch_gdf)
Out[848]:
(2, 2)

La forma correcta de hacerlo es utilizar la fuzzy merging (recuerde que necesitamos the fuzz):

In [851]:
# pick the closest match from nomatch_gdf for a value in nomatch_df
!pip install thefuzz
from thefuzz import process
[(dis,process.extractOne(dis,nomatch_gdf)) for dis in sorted(nomatch_df)]

Requirement already satisfied: thefuzz in c:\users\lenovo\anaconda3\lib\site-packages (0.22.1)
Requirement already satisfied: rapidfuzz<4.0.0,>=3.0.0 in c:\users\lenovo\anaconda3\lib\site-packages (from thefuzz) (3.10.1)
Out[851]:
[('ANCASH+ANTONIORAIMONDI', ('ANCASH+ANTONIORAYMONDI', 95)),
 ('ICA+NAZCA', ('ICA+NASCA', 89))]

Si te sientes cómodo, prepara un diccionario de cambios:

In [853]:
# is this OK?
{dis:process.extractOne(dis,nomatch_gdf)[0] for dis in sorted(nomatch_df)}
Out[853]:
{'ANCASH+ANTONIORAIMONDI': 'ANCASH+ANTONIORAYMONDI', 'ICA+NAZCA': 'ICA+NASCA'}
In [854]:
# then:
changesinDF={dis:process.extractOne(dis,nomatch_gdf)[0] for dis in sorted(nomatch_df)}

Ahora, hacemos reemplazos:

In [859]:
covid_provYear_Adultos_Mayores_w.replace({'location': changesinDF}, inplace=True)

¿Se acabó?

In [862]:
nomatch_df=set(covid_provYear_Adultos_Mayores_w.location)- set(provmap.location)
nomatch_gdf=set(provmap.location)-set(covid_provYear_Adultos_Mayores_w.location) 

[(dis,process.extractOne(dis,nomatch_gdf)) for dis in sorted(nomatch_df)]
Out[862]:
[]

Ahora el merge puede hacerse:

In [865]:
covid_provYear_Adultos_Mayores_map=provmap.merge(covid_provYear_Adultos_Mayores_w, on='location',how='left',indicator='flag')
In [867]:
# check
covid_provYear_Adultos_Mayores_map.info()

<class 'geopandas.geodataframe.GeoDataFrame'>
RangeIndex: 196 entries, 0 to 195
Data columns (total 32 columns):
 #   Column        Non-Null Count  Dtype   
---  ------        --------------  -----   
 0   OBJECTID      196 non-null    float64 
 1   CCDD          196 non-null    object  
 2   CCPP          196 non-null    object  
 3   DEPARTAMEN    196 non-null    object  
 4   PROVINCIA_x   196 non-null    object  
 5   geometry      196 non-null    geometry
 6   location      196 non-null    object  
 7   DEPARTAMENTO  196 non-null    object  
 8   PROVINCIA_y   196 non-null    object  
 9   date202003    196 non-null    float64 
 10  date202004    196 non-null    float64 
 11  date202005    196 non-null    float64 
 12  date202006    196 non-null    float64 
 13  date202007    196 non-null    float64 
 14  date202008    196 non-null    float64 
 15  date202009    196 non-null    float64 
 16  date202010    196 non-null    float64 
 17  date202011    196 non-null    float64 
 18  date202012    196 non-null    float64 
 19  date202101    196 non-null    float64 
 20  date202102    196 non-null    float64 
 21  date202103    196 non-null    float64 
 22  date202104    196 non-null    float64 
 23  date202105    196 non-null    float64 
 24  date202106    196 non-null    float64 
 25  date202107    196 non-null    float64 
 26  date202108    196 non-null    float64 
 27  date202109    196 non-null    float64 
 28  date202110    196 non-null    float64 
 29  date202111    196 non-null    float64 
 30  date202112    196 non-null    float64 
 31  flag          196 non-null    category
dtypes: category(1), float64(23), geometry(1), object(7)
memory usage: 47.9+ KB
In [869]:
# avoid poblems with fillna() 
covid_provYear_Adultos_Mayores_map['flag']=covid_provYear_Adultos_Mayores_map.flag.astype(str)

Podemos deshacernos de algunas columnas:

In [872]:
bye=['DEPARTAMENTO', 'PROVINCIA_y', 'CCPP','CCDD']
covid_provYear_Adultos_Mayores_map.drop(columns=bye,inplace=True)

# keeping
covid_provYear_Adultos_Mayores_map.head()
Out[872]:
OBJECTID DEPARTAMEN PROVINCIA_x geometry location date202003 date202004 date202005 date202006 date202007 ... date202104 date202105 date202106 date202107 date202108 date202109 date202110 date202111 date202112 flag
0 1.0 AMAZONAS CHACHAPOYAS POLYGON ((-77.72614 -5.94354, -77.72486 -5.943... AMAZONAS+CHACHAPOYAS 0.0 0.482759 0.477528 0.557377 0.508197 ... 0.474308 0.468127 0.448387 0.463415 0.566667 0.592593 0.615385 0.555556 0.397351 both
1 2.0 AMAZONAS BAGUA POLYGON ((-78.61909 -4.51001, -78.61802 -4.510... AMAZONAS+BAGUA 0.0 0.250000 0.559524 0.355491 0.396988 ... 0.386986 0.382625 0.401180 0.381818 0.373626 0.443182 0.522613 0.453287 0.400631 both
2 3.0 AMAZONAS BONGARA POLYGON ((-77.72759 -5.1403, -77.72361 -5.1406... AMAZONAS+BONGARA 0.0 0.333333 0.555556 0.238095 0.425926 ... 0.423237 0.442424 0.389610 0.548387 0.366667 0.533333 0.320000 0.187500 0.341463 both
3 4.0 AMAZONAS CONDORCANQUI POLYGON ((-77.81399 -2.99278, -77.81483 -2.995... AMAZONAS+CONDORCANQUI 0.0 0.000000 0.333333 0.524064 0.470363 ... 0.472603 0.513761 0.488095 0.714286 0.500000 0.333333 0.368421 0.333333 0.454545 both
4 5.0 AMAZONAS LUYA POLYGON ((-78.13023 -5.9037, -78.13011 -5.9041... AMAZONAS+LUYA 0.0 0.000000 0.700000 0.569892 0.565217 ... 0.435484 0.411168 0.393162 0.431373 0.422222 0.500000 0.235294 0.210526 0.433333 both

5 rows × 28 columns

In [874]:
# filling with zeroes
covid_provYear_Adultos_Mayores_map.fillna(0,inplace=True)

Podemos guardar este geoDF:

In [877]:
import os
covid_provYear_Adultos_Mayores_map.to_file(os.path.join('maps',"provinciasPeru.gpkg"), layer='provinciasDengue', driver="GPKG")

Exploramos una variable¶

Esta vez, exploramos estadísticamenete una variable en el mapa

In [880]:
# statistics
covid_provYear_Adultos_Mayores_map.date202101.describe()
Out[880]:
count    196.000000
mean       0.403255
std        0.124009
min        0.000000
25%        0.357143
50%        0.418011
75%        0.460248
max        1.000000
Name: date202101, dtype: float64

Una mirada visual:

In [883]:
import seaborn as sea

sea.boxplot(covid_provYear_Adultos_Mayores_map.date202101, color='yellow',orient='h')
Out[883]:
<Axes: xlabel='date202101'>
No description has been provided for this image
In [885]:
from sklearn.preprocessing import QuantileTransformer
qt = QuantileTransformer(n_quantiles=100, random_state=0,output_distribution='normal')
qt_result=qt.fit_transform(covid_provYear_Adultos_Mayores_map[['date202101']])
sea.boxplot(qt_result, color='yellow',orient='h')
Out[885]:
<Axes: >
No description has been provided for this image
In [887]:
covid_provYear_Adultos_Mayores_map['date202101_qt']=qt_result

Spatial Correlation¶

Neighboorhood¶

Podemos calcular la vecindad en un mapa usando diferentes algoritmos:

In [890]:
#!pip install libpysal
from libpysal.weights import Queen, Rook, KNN

# rook

w_rook = Rook.from_dataframe(covid_provYear_Adultos_Mayores_map,use_index=False)
In [891]:
# rook
w_queen = Queen.from_dataframe(covid_provYear_Adultos_Mayores_map,use_index=False)
In [893]:
# k nearest neighbors
w_knn = KNN.from_dataframe(covid_provYear_Adultos_Mayores_map, k=8)

Entendamos las diferencias:

In [897]:
# first one
covid_provYear_Adultos_Mayores_map.head(1)
Out[897]:
OBJECTID DEPARTAMEN PROVINCIA_x geometry location date202003 date202004 date202005 date202006 date202007 ... date202105 date202106 date202107 date202108 date202109 date202110 date202111 date202112 flag date202101_qt
0 1.0 AMAZONAS CHACHAPOYAS POLYGON ((-77.72614 -5.94354, -77.72486 -5.943... AMAZONAS+CHACHAPOYAS 0.0 0.482759 0.477528 0.557377 0.508197 ... 0.468127 0.448387 0.463415 0.566667 0.592593 0.615385 0.555556 0.397351 both 0.775034

1 rows × 29 columns

In [899]:
# amount neighbors of that district
w_rook.neighbors[0]
Out[899]:
[2, 114, 4, 5, 180, 182, 55]
In [901]:
# see
base=covid_provYear_Adultos_Mayores_map[covid_provYear_Adultos_Mayores_map.PROVINCIA_x=="CHACHAPOYAS"].plot()
covid_provYear_Adultos_Mayores_map.iloc[w_rook.neighbors[0] ,].plot(ax=base,facecolor="yellow",edgecolor='k')
covid_provYear_Adultos_Mayores_map.head(1).plot(ax=base,facecolor="red")
Out[901]:
<Axes: >
No description has been provided for this image

Hagamos lo mismo:

In [904]:
w_queen.neighbors[0]
Out[904]:
[2, 114, 4, 5, 180, 182, 55]
In [906]:
base=covid_provYear_Adultos_Mayores_map[covid_provYear_Adultos_Mayores_map.PROVINCIA_x=="CHACHAPOYAS"].plot()
covid_provYear_Adultos_Mayores_map.iloc[w_queen.neighbors[0] ,].plot(ax=base,facecolor="yellow",edgecolor='k')
covid_provYear_Adultos_Mayores_map.head(1).plot(ax=base,facecolor="red")
Out[906]:
<Axes: >
No description has been provided for this image
In [908]:
w_knn.neighbors[0]
Out[908]:
[5, 4, 55, 182, 2, 59, 175, 114]
In [910]:
base=covid_provYear_Adultos_Mayores_map[covid_provYear_Adultos_Mayores_map.PROVINCIA_x=="CHACHAPOYAS"].plot()
covid_provYear_Adultos_Mayores_map.iloc[w_knn.neighbors[0] ,].plot(ax=base,facecolor="yellow",edgecolor='k')
covid_provYear_Adultos_Mayores_map.head(1).plot(ax=base,facecolor="red")
Out[910]:
<Axes: >
No description has been provided for this image

Prestamos atención a los resultados de queen:

In [913]:
# all the neighbors by row
w_queen.neighbors
Out[913]:
{0: [2, 114, 4, 5, 180, 182, 55],
 1: [3, 60, 61, 6],
 2: [0, 143, 3, 4, 6, 182, 175],
 3: [1, 2, 6, 143],
 4: [0, 2, 6, 55, 56],
 5: [0, 178, 180, 182, 175],
 6: [1, 2, 3, 4, 56, 58, 60],
 7: [16, 17, 23, 8, 26, 12, 14],
 8: [17, 7, 23],
 9: [16, 90, 13],
 10: [16, 26, 12, 13],
 11: [96, 129, 128, 16, 17, 20, 23, 89, 91],
 12: [16, 26, 10, 7],
 13: [16, 19, 26, 90, 9, 10],
 14: [24, 17, 26, 7],
 15: [24, 25, 18, 21],
 16: [7, 9, 10, 11, 12, 13, 23, 90, 91],
 17: [128, 23, 7, 8, 11, 14],
 18: [22, 24, 25, 26, 15],
 19: [13, 26, 22, 90, 93],
 20: [128, 129, 11, 134],
 21: [119, 24, 121, 123, 25, 15],
 22: [18, 19, 119, 25, 26, 93],
 23: [16, 17, 7, 8, 11],
 24: [18, 21, 121, 26, 123, 14, 15],
 25: [18, 21, 22, 119, 15],
 26: [7, 10, 12, 13, 14, 18, 19, 22, 24],
 27: [33, 69, 75, 28, 29, 30, 31],
 28: [32, 75, 46, 47, 48, 50, 52, 27, 30],
 29: [48, 33, 73, 27, 31, 30, 41],
 30: [48, 27, 28, 29],
 31: [33, 69, 73, 27, 76, 29],
 32: [42, 52, 28, 46],
 33: [27, 29, 31],
 34: [35, 149, 38, 40, 172],
 35: [34, 36, 37, 38, 39, 40],
 36: [48, 49, 35, 100, 39, 41, 47],
 37: [35, 38, 39],
 38: [34, 35, 37, 39, 168, 73, 74, 172],
 39: [35, 36, 37, 38, 73, 74, 41],
 40: [34, 35, 148, 149, 150],
 41: [48, 49, 36, 39, 73, 29],
 42: [32, 82, 52, 85, 43, 45, 46],
 43: [42, 51, 52, 85],
 44: [51, 85, 47],
 45: [42, 75, 108, 46, 81, 82, 84, 86],
 46: [32, 42, 75, 28, 45],
 47: [36, 100, 101, 44, 48, 50, 51, 85, 28],
 48: [49, 36, 41, 28, 29, 30, 47],
 49: [48, 41, 36],
 50: [51, 52, 28, 47],
 51: [50, 52, 85, 43, 44, 47],
 52: [32, 50, 51, 42, 43, 28],
 53: [64, 54, 55, 57, 122, 59, 62],
 54: [114, 53, 117, 120, 122, 62],
 55: [0, 114, 4, 53, 56, 59, 62],
 56: [65, 4, 6, 55, 58, 59, 124, 125],
 57: [64, 113, 115, 53, 118, 122, 63],
 58: [56, 60, 125, 6],
 59: [64, 65, 53, 55, 56, 63],
 60: [1, 125, 6, 58, 156, 61, 126],
 61: [1, 156, 60],
 62: [114, 53, 54, 55],
 63: [64, 65, 115, 57, 59, 124],
 64: [57, 59, 53, 63],
 65: [56, 59, 124, 63],
 66: [127],
 67: [69, 70, 76, 78, 79],
 68: [71, 72, 73, 76, 78],
 69: [67, 75, 27, 76, 79, 31],
 70: [146, 67, 75, 77, 78, 79],
 71: [169, 68, 72, 73, 74],
 72: [68, 164, 71, 169, 78],
 73: [68, 38, 39, 71, 41, 74, 76, 29, 31],
 74: [169, 38, 39, 168, 73, 71],
 75: [193, 69, 70, 108, 45, 46, 79, 146, 27, 28],
 76: [67, 68, 69, 73, 78, 31],
 77: [78, 146, 70],
 78: [67, 68, 164, 70, 72, 76, 77, 146],
 79: [75, 67, 69, 70],
 80: [103, 136, 81, 82, 83, 84, 85, 86],
 81: [80, 82, 84, 45],
 82: [80, 81, 85, 42, 45],
 83: [80, 99, 85, 102, 136],
 84: [80, 81, 45, 86],
 85: [98, 101, 102, 42, 43, 44, 47, 80, 82, 51, 83],
 86: [80, 84, 103, 108, 45],
 87: [96, 97, 88, 89, 92, 94],
 88: [96, 87, 151, 152, 94],
 89: [96, 97, 87, 11, 91, 92],
 90: [16, 19, 93, 9, 91, 92, 13],
 91: [16, 89, 90, 11, 92],
 92: [194, 142, 176, 87, 184, 89, 90, 91, 93, 94, 95],
 93: [19, 22, 119, 184, 90, 92],
 94: [87, 151, 153, 88, 92, 95],
 95: [192, 194, 153, 92, 94],
 96: [129, 97, 135, 11, 87, 88, 89, 152],
 97: [96, 89, 87],
 98: [101, 100, 85, 102],
 99: [136, 83, 131, 102],
 100: [98, 36, 101, 47],
 101: [98, 100, 85, 47],
 102: [83, 98, 99, 85],
 103: [80, 86, 136, 108, 104, 111],
 104: [103, 136, 106, 108, 111],
 105: [153, 106, 107, 108, 109],
 106: [108, 133, 136, 105, 104, 109, 110],
 107: [153, 151, 105, 109, 110],
 108: [193, 103, 104, 105, 106, 75, 45, 86, 153],
 109: [105, 106, 107, 110],
 110: [130, 132, 133, 151, 106, 107, 109],
 111: [136, 104, 103],
 112: [113, 123, 116, 117],
 113: [112, 117, 118, 57, 122],
 114: [0, 180, 55, 54, 119, 120, 62],
 115: [57, 124, 118, 63],
 116: [112, 121, 123, 117],
 117: [112, 113, 116, 54, 120, 121, 122],
 118: [113, 115, 57],
 119: [114, 180, 21, 22, 184, 121, 120, 93, 25],
 120: [114, 117, 54, 119, 121],
 121: [116, 21, 117, 119, 24, 123, 120],
 122: [113, 117, 53, 54, 57],
 123: [112, 116, 21, 24, 121],
 124: [65, 115, 56, 125, 126, 63],
 125: [60, 56, 58, 124, 126],
 126: [124, 161, 156, 157, 154, 60, 125],
 127: [66, 130, 132, 133, 131],
 128: [17, 11, 20, 134],
 129: [96, 20, 134, 135, 11],
 130: [132, 133, 110, 127],
 131: [136, 99, 133, 127],
 132: [130, 134, 151, 110, 127],
 133: [130, 131, 136, 106, 110, 127],
 134: [128, 129, 132, 20, 135, 151],
 135: [96, 129, 134, 151, 152],
 136: [99, 131, 133, 103, 104, 106, 111, 80, 83],
 137: [144, 139, 140, 141],
 138: [143, 179, 183, 139, 141, 175],
 139: [137, 138, 141, 143],
 140: [144, 137, 141],
 141: [192, 183, 137, 138, 139, 140, 142],
 142: [192, 176, 194, 181, 183, 92, 141],
 143: [2, 3, 138, 139, 175],
 144: [137, 140],
 145: [193, 146, 147, 164, 173],
 146: [193, 145, 164, 70, 75, 77, 78],
 147: [145, 195, 193],
 148: [162, 149, 166, 150, 40, 186, 187],
 149: [34, 162, 148, 40, 172],
 150: [40, 187, 148],
 151: [132, 134, 135, 107, 110, 88, 153, 152, 94],
 152: [96, 151, 88, 135],
 153: [192, 193, 105, 107, 108, 151, 94, 95],
 154: [161, 126, 155, 157, 158, 159],
 155: [154, 156, 157, 159],
 156: [157, 155, 60, 61, 126],
 157: [154, 155, 156, 126],
 158: [160, 161, 154, 159],
 159: [160, 158, 154, 155, 189, 190],
 160: [158, 190, 159],
 161: [154, 126, 158],
 162: [148, 149, 166, 167, 172],
 163: [164, 167, 168, 169, 171, 172],
 164: [163, 72, 169, 171, 173, 78, 145, 146],
 165: [174, 166],
 166: [162, 148, 165, 186, 188],
 167: [162, 163, 170, 171, 172],
 168: [163, 38, 169, 74, 172],
 169: [163, 164, 71, 72, 74, 168],
 170: [167],
 171: [163, 164, 173, 167],
 172: [34, 162, 163, 149, 38, 167, 168],
 173: [145, 171, 164],
 174: [165],
 175: [2, 5, 138, 143, 177, 178, 179, 182],
 176: [177, 178, 180, 181, 184, 92, 142],
 177: [176, 178, 179, 181, 175],
 178: [176, 177, 180, 5, 175],
 179: [177, 181, 183, 138, 175],
 180: [0, 176, 178, 114, 5, 119, 184],
 181: [176, 177, 179, 183, 142],
 182: [0, 2, 5, 175],
 183: [179, 181, 138, 141, 142],
 184: [176, 180, 119, 92, 93],
 185: [186, 187, 188],
 186: [148, 166, 185, 187, 188],
 187: [148, 150, 185, 186, 188],
 188: [185, 186, 187, 166],
 189: [159, 190, 191],
 190: [160, 189, 159],
 191: [189],
 192: [193, 194, 153, 141, 142, 95],
 193: [192, 195, 75, 108, 145, 146, 147, 153],
 194: [192, 92, 142, 95],
 195: [193, 147]}
In [915]:
# the matrix of neighboorhood:

pd.DataFrame(*w_queen.full()).astype(int) # 1 means both are neighbors
Out[915]:
0 1 2 3 4 5 6 7 8 9 ... 186 187 188 189 190 191 192 193 194 195
0 0 0 1 0 1 1 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
1 0 0 0 1 0 0 1 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
2 1 0 0 1 1 0 1 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
3 0 1 1 0 0 0 1 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
4 1 0 1 0 0 0 1 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
191 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 1 0 0 0 0 0 0
192 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 1 1 0
193 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 1 0 0 1
194 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 1 0 0 0
195 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 1 0 0

196 rows × 196 columns

In [917]:
# pct of neighboorhood (density)
w_queen.pct_nonzero
Out[917]:
2.7332361516034984
In [919]:
# a province with NO neighbor?
w_queen.islands
Out[919]:
[]

Moran's correlation¶

Necesitamos la matriz de vecindad (la matriz de peso) para calcular la correlación espacial: si el valor de la variable está correlacionado con los valores de sus vecinos, lo que demuestra un efecto espacial.

In [922]:
# needed for spatial correlation
w_queen.transform = 'R'
In [924]:
pd.DataFrame(*w_queen.full()).sum(axis=1) # 1 means both are neighbors
Out[924]:
0      1.0
1      1.0
2      1.0
3      1.0
4      1.0
      ... 
191    1.0
192    1.0
193    1.0
194    1.0
195    1.0
Length: 196, dtype: float64

La correlación es medida por la "Moran's I statitic":

In [927]:
#!pip install esda

from esda.moran import Moran

moranCOVID = Moran(covid_provYear_Adultos_Mayores_map['date202101_qt'], w_queen)
moranCOVID.I,moranCOVID.p_sim
Out[927]:
(0.0014287175342288713, 0.424)

La Moran's I es significante. Veamos:

In [930]:
#!pip install splot

from splot.esda import moran_scatterplot
import matplotlib.pyplot as plt

fig, ax = moran_scatterplot(moranCOVID)
ax.set_xlabel('Covid_PAM_share')
ax.set_ylabel('SpatialLag_Covid_PAM_share')
Out[930]:
Text(0, 0.5, 'SpatialLag_Covid_PAM_share')
No description has been provided for this image

Local Spatial Correlation¶

Es posible que se detecte una correlación no significante. Veamos esos valores:

In [933]:
# The scatterplot with local info

from esda.moran import Moran_Local

# calculate Moran_Local and plot
lisaCOVID = Moran_Local(y=covid_provYear_Adultos_Mayores_map['date202101_qt'], w=w_knn,seed=2022)
fig, ax = moran_scatterplot(lisaCOVID,p=0.05)
ax.set_xlabel('COVID_PAM_share')
ax.set_ylabel('SpatialLag_COVID_PAM_share');
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In [935]:
from splot.esda import plot_local_autocorrelation
plot_local_autocorrelation(lisaCOVID, covid_provYear_Adultos_Mayores_map,'date202101_qt')
plt.show()
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In [937]:
# the map with the spots and outliers

from splot.esda import lisa_cluster
f, ax = plt.subplots(1, figsize=(12, 12))
plt.title('Spots and Outliers')
fig = lisa_cluster(lisaCOVID, 
                   covid_provYear_Adultos_Mayores_map,ax=ax,
                   legend_kwds={'loc': 'center left', 
                                'bbox_to_anchor': (0.7, 0.6)})
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Añadimos esta data al gdf:

In [940]:
# quadrant
lisaCOVID.q
Out[940]:
array([4, 2, 1, 1, 1, 2, 1, 4, 3, 3, 3, 3, 2, 4, 3, 1, 3, 3, 2, 4, 4, 2,
       2, 3, 2, 2, 1, 1, 4, 1, 2, 4, 4, 2, 3, 3, 4, 3, 3, 3, 3, 4, 3, 3,
       3, 1, 4, 3, 2, 4, 4, 3, 3, 1, 4, 3, 4, 4, 1, 1, 4, 2, 4, 1, 2, 2,
       2, 4, 2, 2, 4, 1, 4, 2, 4, 1, 3, 3, 4, 2, 1, 2, 1, 1, 1, 1, 1, 1,
       1, 3, 3, 4, 2, 3, 1, 1, 3, 3, 1, 2, 4, 3, 1, 1, 4, 2, 3, 3, 1, 3,
       3, 2, 4, 3, 2, 4, 2, 2, 2, 1, 4, 1, 4, 2, 2, 2, 1, 4, 4, 3, 4, 1,
       3, 4, 4, 4, 2, 1, 1, 1, 2, 2, 1, 1, 1, 4, 4, 2, 4, 2, 3, 1, 3, 1,
       1, 1, 1, 2, 1, 2, 1, 1, 4, 4, 1, 3, 3, 3, 1, 2, 2, 4, 4, 3, 3, 1,
       1, 1, 3, 2, 2, 1, 1, 1, 3, 3, 3, 4, 3, 1, 1, 2, 1, 2, 1, 3])
In [942]:
# significance
lisaCOVID.p_sim
Out[942]:
array([0.389, 0.102, 0.076, 0.103, 0.171, 0.036, 0.086, 0.069, 0.08 ,
       0.382, 0.269, 0.052, 0.309, 0.04 , 0.105, 0.484, 0.05 , 0.215,
       0.144, 0.035, 0.016, 0.04 , 0.298, 0.018, 0.124, 0.004, 0.427,
       0.44 , 0.397, 0.288, 0.367, 0.257, 0.426, 0.337, 0.249, 0.251,
       0.209, 0.291, 0.388, 0.248, 0.272, 0.17 , 0.336, 0.244, 0.308,
       0.093, 0.448, 0.114, 0.294, 0.431, 0.061, 0.431, 0.305, 0.203,
       0.184, 0.374, 0.38 , 0.213, 0.217, 0.447, 0.36 , 0.151, 0.095,
       0.389, 0.244, 0.428, 0.378, 0.271, 0.369, 0.448, 0.267, 0.49 ,
       0.445, 0.495, 0.321, 0.231, 0.392, 0.37 , 0.334, 0.474, 0.114,
       0.032, 0.043, 0.233, 0.098, 0.434, 0.031, 0.348, 0.298, 0.069,
       0.143, 0.016, 0.433, 0.199, 0.382, 0.04 , 0.056, 0.318, 0.361,
       0.107, 0.33 , 0.282, 0.149, 0.385, 0.424, 0.437, 0.314, 0.46 ,
       0.319, 0.446, 0.45 , 0.303, 0.225, 0.411, 0.221, 0.125, 0.031,
       0.391, 0.469, 0.015, 0.097, 0.243, 0.188, 0.238, 0.48 , 0.381,
       0.119, 0.432, 0.034, 0.42 , 0.418, 0.363, 0.132, 0.281, 0.162,
       0.09 , 0.13 , 0.187, 0.01 , 0.02 , 0.189, 0.045, 0.16 , 0.084,
       0.18 , 0.392, 0.419, 0.321, 0.021, 0.372, 0.042, 0.437, 0.1  ,
       0.258, 0.095, 0.48 , 0.325, 0.196, 0.139, 0.17 , 0.275, 0.19 ,
       0.041, 0.336, 0.295, 0.006, 0.103, 0.323, 0.423, 0.022, 0.133,
       0.162, 0.107, 0.411, 0.007, 0.102, 0.2  , 0.238, 0.241, 0.024,
       0.475, 0.246, 0.041, 0.04 , 0.102, 0.046, 0.141, 0.015, 0.298,
       0.243, 0.222, 0.182, 0.117, 0.373, 0.318, 0.294])
In [944]:
# quadrant: 1 HH,  2 LH,  3 LL,  4 HL
pd.Series(lisaCOVID.q).value_counts()
Out[944]:
1    59
3    49
4    44
2    44
Name: count, dtype: int64

La información en LisaCOVID.q no puede usarse de manera correcta, necesitamos añadir si la correlación local spatial es significante:

In [947]:
covid_provYear_Adultos_Mayores_map['COVID_quadrant']=[l if p <0.05 else 0 for l,p in zip(lisaCOVID.q,lisaCOVID.p_sim)  ]
covid_provYear_Adultos_Mayores_map['COVID_quadrant'].value_counts()
Out[947]:
COVID_quadrant
0    167
2      8
4      8
1      8
3      5
Name: count, dtype: int64

Ahora recodificamos

In [950]:
labels = [ '0 no_sig', '1 hotSpot', '2 coldOutlier', '3 coldSpot', '4 hotOutlier']

covid_provYear_Adultos_Mayores_map['COVID_quadrant_names']=[labels[i] for i in covid_provYear_Adultos_Mayores_map['COVID_quadrant']]

covid_provYear_Adultos_Mayores_map['COVID_quadrant_names'].value_counts()
Out[950]:
COVID_quadrant_names
0 no_sig         167
2 coldOutlier      8
4 hotOutlier       8
1 hotSpot          8
3 coldSpot         5
Name: count, dtype: int64

Hacemos replot:

In [953]:
from matplotlib import colors
myColMap = colors.ListedColormap([ 'ghostwhite', 'red', 'green', 'black','orange'])




f, ax = plt.subplots(1, figsize=(12,12))


plt.title('Spots and Outliers')

covid_provYear_Adultos_Mayores_map.plot(column='COVID_quadrant_names', 
                categorical=True,
                cmap=myColMap,
                linewidth=0.1, 
                edgecolor='white',
                legend=True,
                legend_kwds={'loc': 'center left', 
                             'bbox_to_anchor': (0.7, 0.6)},
                ax=ax)
# Remove axis
ax.set_axis_off()
# Display the map
plt.show()
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Bivariate LISA¶

In [956]:
#!pip install branca
#!pip install folium

covid_provYear_Adultos_Mayores_map.explore("COVID_quadrant_names", categorical=True,tooltip='location',cmap=myColMap)
Out[956]:
Make this Notebook Trusted to load map: File -> Trust Notebook
In [958]:
import folium

map1=covid_provYear_Adultos_Mayores_map[covid_provYear_Adultos_Mayores_map.COVID_quadrant_names=='1 hotSpot']
map2=covid_provYear_Adultos_Mayores_map[covid_provYear_Adultos_Mayores_map.COVID_quadrant_names=='2 coldOutlier']
map3=covid_provYear_Adultos_Mayores_map[covid_provYear_Adultos_Mayores_map.COVID_quadrant_names=='3 coldSpot']
map4=covid_provYear_Adultos_Mayores_map[covid_provYear_Adultos_Mayores_map.COVID_quadrant_names=='4 hotOutlier']

m = map1.explore(
    color="red",  
    tooltip=False,  # hide tooltip
    popup=["location"],  # (on-click)
    name="hotSpot"  # name of the layer in the map
)

map2.explore(
    m=m, # notice
    color="green",  
    tooltip=False,  
    popup=["location"],
    name="coldOutlier"
)

map3.explore(
    m=m,
    color="black",  
    tooltip=False,  
    popup=["location"],
    name="coldSpot", 
)

map4.explore(
    m=m,
    color="orange", 
    tooltip=False,  
    popup=["location"],
    name="hotOutlier", 
)

folium.TileLayer("CartoDB positron", show=False).add_to(m)  # use folium to add alternative tiles
folium.LayerControl(collapsed=True).add_to(m)  # use folium to add layer control

m  # show map
Out[958]:
Make this Notebook Trusted to load map: File -> Trust Notebook
In [960]:
#from esda.moran import Moran_BV, Moran_Local_BV
from esda.moran import Moran_BV

mbi = Moran_BV(covid_provYear_Adultos_Mayores_map['date202003'],  covid_provYear_Adultos_Mayores_map['date202103'],  w_queen)
mbi.I,mbi.p_sim
Out[960]:
(-0.04994328750911756, 0.093)
In [962]:
# The scatterplot with local info
from esda.moran import Moran_Local_BV

# calculate Moran_Local and plot
lisaCOVID_bv = Moran_Local_BV(y=covid_provYear_Adultos_Mayores_map['date202003'],
                               x=covid_provYear_Adultos_Mayores_map['date202103'],
                               w=w_queen)

fig, ax = moran_scatterplot(lisaCOVID_bv, p=0.05,aspect_equal=True)

ax.set_xlabel('COVID_2021')
ax.set_ylabel('SpatialLag_COVID_2020')
plt.show()
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In [964]:
covid_provYear_Adultos_Mayores_map['COVID_quadrant_21_22']=[l if p <0.05 else 0 for l,p in zip(lisaCOVID_bv.q,lisaCOVID_bv.p_sim)  ]

labels = [ '0 no_sig', '1 hotSpot', '2 coldOutlier', '3 coldSpot', '4 hotOutlier']

covid_provYear_Adultos_Mayores_map['COVID_quadrant_21_22_names']=[labels[i] for i in covid_provYear_Adultos_Mayores_map['COVID_quadrant_21_22']]
In [966]:
# see new columns
covid_provYear_Adultos_Mayores_map
Out[966]:
OBJECTID DEPARTAMEN PROVINCIA_x geometry location date202003 date202004 date202005 date202006 date202007 ... date202109 date202110 date202111 date202112 flag date202101_qt COVID_quadrant COVID_quadrant_names COVID_quadrant_21_22 COVID_quadrant_21_22_names
0 1.0 AMAZONAS CHACHAPOYAS POLYGON ((-77.72614 -5.94354, -77.72486 -5.943... AMAZONAS+CHACHAPOYAS 0.00 0.482759 0.477528 0.557377 0.508197 ... 0.592593 0.615385 0.555556 0.397351 both 0.775034 0 0 no_sig 0 0 no_sig
1 2.0 AMAZONAS BAGUA POLYGON ((-78.61909 -4.51001, -78.61802 -4.510... AMAZONAS+BAGUA 0.00 0.250000 0.559524 0.355491 0.396988 ... 0.443182 0.522613 0.453287 0.400631 both -0.704117 0 0 no_sig 4 4 hotOutlier
2 3.0 AMAZONAS BONGARA POLYGON ((-77.72759 -5.1403, -77.72361 -5.1406... AMAZONAS+BONGARA 0.00 0.333333 0.555556 0.238095 0.425926 ... 0.533333 0.320000 0.187500 0.341463 both 0.084275 0 0 no_sig 4 4 hotOutlier
3 4.0 AMAZONAS CONDORCANQUI POLYGON ((-77.81399 -2.99278, -77.81483 -2.995... AMAZONAS+CONDORCANQUI 0.00 0.000000 0.333333 0.524064 0.470363 ... 0.333333 0.368421 0.333333 0.454545 both 1.521722 0 0 no_sig 0 0 no_sig
4 5.0 AMAZONAS LUYA POLYGON ((-78.13023 -5.9037, -78.13011 -5.9041... AMAZONAS+LUYA 0.00 0.000000 0.700000 0.569892 0.565217 ... 0.500000 0.235294 0.210526 0.433333 both 1.625682 0 0 no_sig 4 4 hotOutlier
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
191 192.0 TUMBES ZARUMILLA POLYGON ((-80.28521 -3.41276, -80.28406 -3.412... TUMBES+ZARUMILLA 0.25 0.368421 0.295652 0.345382 0.494565 ... 0.440476 0.328358 0.255319 0.369231 both -0.242349 0 0 no_sig 1 1 hotSpot
192 193.0 UCAYALI CORONEL PORTILLO POLYGON ((-74.47145 -7.27617, -74.47052 -7.277... UCAYALI+CORONELPORTILLO 0.00 0.438525 0.412000 0.406504 0.354535 ... 0.488372 0.472222 0.346939 0.416667 both 0.646177 0 0 no_sig 0 0 no_sig
193 194.0 UCAYALI ATALAYA POLYGON ((-73.18146 -9.41174, -73.13475 -9.411... UCAYALI+ATALAYA 0.00 0.000000 0.000000 0.571429 0.397959 ... 0.333333 0.000000 0.333333 0.000000 both -1.734355 0 0 no_sig 0 0 no_sig
194 195.0 UCAYALI PADRE ABAD POLYGON ((-75.43663 -8.22999, -75.43651 -8.230... UCAYALI+PADREABAD 0.00 0.250000 0.356589 0.410256 0.301435 ... 0.250000 0.545455 0.533333 0.500000 both 0.915560 0 0 no_sig 4 4 hotOutlier
195 196.0 UCAYALI PURUS POLYGON ((-70.6138 -9.87339, -70.6214 -9.87808... UCAYALI+PURUS 0.00 0.000000 0.000000 0.000000 0.210526 ... 0.000000 0.000000 0.000000 0.000000 both -1.882798 0 0 no_sig 0 0 no_sig

196 rows × 33 columns

In [968]:
from matplotlib import colors
myColMap = colors.ListedColormap([ 'ghostwhite', 'red', 'green', 'black','orange'])




f, ax = plt.subplots(1, figsize=(12,12))


plt.title('Spots and Outliers')

covid_provYear_Adultos_Mayores_map.plot(column='COVID_quadrant_21_22_names', 
                categorical=True,
                cmap=myColMap,
                linewidth=0.1, 
                edgecolor='white',
                legend=True,
                legend_kwds={'loc': 'center left', 
                             'bbox_to_anchor': (0.7, 0.6)},
                ax=ax)
# Remove axis
ax.set_axis_off()
# Display the map
plt.show()
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